Harnessing Quantum Resources in Superconducting Devices for Computing and Sensing

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Volume Title
School of Science | Doctoral thesis (article-based) | Defence date: 2024-01-19
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Author
Date
2024
Department
Teknillisen fysiikan laitos
Department of Applied Physics
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Mcode
Degree programme
Language
en
Pages
113 + app. 103
Series
Aalto University publication series DOCTORAL THESES, 4/2024
Abstract
Quantum resources play a pivotal role in the emerging field of quantum technologies, underpinning the development of quantum computers, communication systems, and sensing devices with transformative capabilities beyond classical counterparts. In this thesis, key quantum resources, such as entanglement and coherency, are investigated with an emphasis on superconducting metamaterials, including Josephson Parametric Amplifiers, Travelling Wave Parametric Amplifiers, and artificial transmon atoms. The presented thesis strives to explore, unify, and harness the quantum resources in quantum sensing and computing tasks. The initial cluster of studies places a spotlight on the experimental preparation of quantum states enriched with quantum resources. The thesis presents the generation of multipartite quantum entanglement using innovative pump tone techniques applied to the Josephson parametric system, expanding the possibilities for microwave control of the entanglement structure and demonstrating for the first time genuine entanglement between four microwave modes. The generation of frequency-entangled photons with record-breaking 4 GHz frequency separation is presented for distributed Josephson metamaterial, highlighting the practical implications of entangled microwave photons in broadband quantum information processing. Besides, the thesis presents the protocol for preparation of multi-qubit states with target amplitudes, which features polylogarithmic scaling in the number of encoded parameters. The protocol demonstrates its effectiveness with large-scale circuits up to 100 qubits, which are studied numerically. The presented work advances phase estimation protocols for quantum sensing, optimizing resource utilization and sensitivity, particularly in the realm of magnetic field measurements. It further explores the effectiveness of separable and entangled states for magnetometry by conducting experiments on existing quantum hardware through cloud-based IBM quantum systems. A novel sensing algorithm for multi-level artificial atoms emerges from this exploration, designed to maximize the utilization of available quantum resources, which is numerically investigated. Finally, the thesis presents a study of the hybrid quantum algorithm for solving large linear systems of equations, showcases the current state of intermediate-scale quantum computers, and proposes the benchmark for future hardware developments. It introduces a classification scheme based on entanglement structure and successfully implements a record-breaking 217-dimensional problem on IBM quantum processors.
Description
Supervising professor
Hakonen, Pertti, Prof., Aalto University School of Science, Department of Applied Physics, Finland
Keywords
quantum technologies, quantum computers, quantum sensing, superconducting metamaterials, Josephson parametric amplifiers, travelling wave parametric amplifiers, transmon, quantum information processing, magnetometry, IBM quantum systems, quantum algorithms, intermediate-scale quantum computers
Other note
Parts
  • [Publication 1]: T. Elo, T. S. Abhilash, M. R. Perelshtein, I. Lilja, E. V. Korostylev, and P. J. Hakonen. Broadband lumped-element Josephson parametric amplifier with single-step lithography. Applied Physics Letters, 114, 152601, April 2019.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-201905062708
    DOI: 10.1063/1.5086091 View at publisher
  • [Publication 2]: K. V. Petrovnin, M. R. Perelshtein, T. Korkalainen, V. Vesterinen, I. Lilja, G. S. Paraoanu, P.J. Hakonen. Generation and structuring of multipartite entanglement in Josephson parametric system. Advanced Quantum Technologies, 6, 2200031, October 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202210266244
    DOI: 10.1002/qute.202200031 View at publisher
  • [Publication 3]: M. R. Perelshtein, K. V. Petrovnin, V. Vesterinen, S. Hamedani Raja, I. Lilja, M. Will, A. Savin, S. Simbierowicz, R. N. Jabdaraghi, J.S. Lehtinen, L. Grönberg, J. Hassel, M. P. Prunnila, J. Govenius, G. S. Paraoanu, and P. J. Hakonen. Broadband continuous variable entanglement generation using a Kerr-free Josephson metamaterial. Physical Review Applied, 18, 024063, August 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202211096435
    DOI: 10.1103/PhysRevApplied.18.024063 View at publisher
  • [Publication 4]: Ar. A. Melnikov, A. A. Termanova, S. V. Dolgov, F. Neukart, and M. R. Perelshtein. Quantum state preparation using tensor networks. Quantum Science and Technology, 8, 035027, June 2023.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202308014646
    DOI: 10.1088/2058-9565/acd9e7 View at publisher
  • [Publication 5]: N. N. Gusarov, M. R. Perelshtein, P. J. Hakonen, and G. S. Paraoanu. Optimized emulation of quantum magnetometry via superconducting qubits. Physical Review A, 107, 052609, May 2023.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202308014504
    DOI: 10.1103/PhysRevA.107.052609 View at publisher
  • [Publication 6]: M. R. Perelshtein, N. S. Kirsanov, V. V. Zemlyanov, A. V. Lebedev, G. Blatter, V. M. Vinokur, and G. B. Lesovik. Linear ascending metrological algorithm. Physical Review Research, 3, 013257, March 2021.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202105056474
    DOI: 10.1103/PhysRevResearch.3.013257 View at publisher
  • [Publication 7]: M. R. Perelshtein, A. I. Pakhomchik, Ar. A. Melnikov, A. A. Novikov, A. Glatz, G. S. Paraoanu, V. M. Vinokur, G. B. Lesovik. Solving large-scale linear systems of equations by a quantum hybrid algorithm. Annalen der Physik, 534, 2200082, May 2022.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202208104543
    DOI: 10.1002/andp.202200082 View at publisher
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https://urn.fi/URN:ISBN:978-952-64-1615-1
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[diss] Perustieteiden korkeakoulu / SCI